8 citations · 16 across the 5 of their papers we have counts for
5 papers
Improving FIM Code Completions via Context & Curriculum Based Learning
Hitesh Sagtani, Rishabh Mehrotra, Beyang Liu
Fill-in-the-Middle (FIM) models play a vital role in code completion tasks, leveraging both prefix and suffix context to provide more accurate and contextually relevant suggestions…
Crafting Tomorrow: The Influence of Design Choices on Fresh Content in Social Media Recommendation
Srijan Saket, Mohit Agarwal, Rishabh Mehrotra
The rise in popularity of social media platforms, has resulted in millions of new, content pieces being created every day. This surge in content creation underscores the need to pa…
AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations
Jan Hartman, Rishabh Mehrotra, Hitesh Sagtani +5
In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task of providing code recommendations in multiple formats to…
On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
Olivier Jeunen, Hitesh Sagtani, Himanshu Doi +7
Practitioners who wish to build real-world applications that rely on ranking models, need to decide which modelling paradigm to follow. This is not an easy choice to make, as the r…
Ad-load Balancing via Off-policy Learning in a Content Marketplace
Hitesh Sagtani, Madan Jhawar, Rishabh Mehrotra +1
Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and re…